Illuminant Invariant Descriptors for Color Texture Classification

  • Claudio Cusano
  • Paolo Napoletano
  • Raimondo Schettini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7786)

Abstract

In this paper we present a novel descriptor for color texture analysis specially designed to deal with changes in illumination in imaging. The descriptor, that we called Intensity Color Contrast Descriptor (ICCD), is based on a combination of the LBP approach with a measure of color contrast defined as the angle between two color vectors in an orthonormal color space. The ICCD robustness with respect to global changes in lighting conditions has been experimentally demonstrated by comparing it on standard data sets against several other in the state of the art.

Keywords

Color texture classification Illuminant invariant descriptors 

References

  1. 1.
    Kandaswamy, U., Adjeroh, D., Schuckers, S., Hanbury, A.: Robust color texture features under varying illumination conditions. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 58–68 (2012)CrossRefGoogle Scholar
  2. 2.
    Porebski, A., Vandenbroucke, N., Macaire, L.: Supervised texture classification: color space or texture feature selection? Pattern Analysis and Applications, 1–18 (2012)Google Scholar
  3. 3.
    Mänepää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recognition 37(8), 1629–1640 (2004)CrossRefGoogle Scholar
  4. 4.
    Shi, L., Funt, B.: Quaternion color texture segmentation. Computer Vision and Image Understanding 107(12), 88–96 (2007); Special issue on color image processingCrossRefGoogle Scholar
  5. 5.
    Jain, A., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 153–158 (1997)CrossRefGoogle Scholar
  6. 6.
    Livingstone, M., Hubel, D.: Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science 240, 740–749 (1988)CrossRefGoogle Scholar
  7. 7.
    Landy, M.S., Graham, N.: Visual perception of texture. The Visual Neurosciences, 1106–1118 (2004)Google Scholar
  8. 8.
    Papathomas, T.V., Kashi, R.S., Gorea, A.: A human vision based computational model for chromatic texture segregation. Trans. Sys. Man Cyber. Part B 27(3), 428–440 (1997)CrossRefGoogle Scholar
  9. 9.
    Drimbarean, A., Whelan, P.: Experiments in colour texture analysis. Pattern Recognition Letters 22(10), 1161–1167 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Poirson, A.B., Wandell, B.A.: The appearance of colored patterns: Pattern-color separability. J. Opt. Soc. Am. A 10, 2458–2470 (1993)CrossRefGoogle Scholar
  11. 11.
    DeYoe, E.A., Essen, D.C.V.: Concurrent processing streams in monkey visual cortex. TINS 11(5), 219–226 (1988)Google Scholar
  12. 12.
    Poirson, A.B., Wandell, B.A.: Pattern-color separable pathways predict sensitivity to simple colored patterns. Vision Research 36, 515–526 (1996)CrossRefGoogle Scholar
  13. 13.
    Mojsilovic, A., Kovacevic, J., Hu, J., Safranek, R.J., Ganapathy, S.K.: Matching and retrieval based on the vocabulary and grammar of color patterns. IEEE Trans. Image Processing 9, 38–54 (2000)CrossRefGoogle Scholar
  14. 14.
    Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial College Press, London (2008)CrossRefGoogle Scholar
  15. 15.
    Haralick, R.: Statistical and structural approaches to texture. Proceedings of the IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  16. 16.
    Vilnrotter, F.M., Nevatia, R., Price, K.E.: Structural analysis of natural textures. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 76–89 (1986)CrossRefGoogle Scholar
  17. 17.
    Azencott, R., Wang, J.-P., Younes, L.: Texture classification using windowed fourier filters. IEEE Trans. Pattern Anal. Mach. Intell. 19, 148–153 (1997)CrossRefGoogle Scholar
  18. 18.
    Randen, T., Husøy, J.H.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 291–310 (1999)CrossRefGoogle Scholar
  19. 19.
    Chen, Y.Q., Nixon, M.S., Thomas, D.W.: Statistical geometrical features for texture classification. Pattern Recognition 28(4), 537–552 (1995)CrossRefGoogle Scholar
  20. 20.
    Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recognition 37(5), 965–976 (2004)CrossRefGoogle Scholar
  21. 21.
    Tang, X.: Texture information in run-length matrices. IEEE Transactions on Image Processing 7(11), 1602–1609 (1998)CrossRefGoogle Scholar
  22. 22.
    Unser, M.: Sum and difference histograms for texture classification. IEEE Trans. Pattern Anal. Mach. Intell. 8(1), 118–125 (1986)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Chellappa, R., Chatterjee, S.: Classification of textures using gaussian markov random fields. IEEE Transactions on Acoustics, Speech and Signal Processing 33(4), 959–963 (1985)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Hernandez, O.J., Cook, J., Griffin, M., Rama, C.D., Mcgovern, M.: Classification of color textures with random field models and neural networks. Journal of Computer Science & Technology 5(3), 150–157 (2005)Google Scholar
  25. 25.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol. 40, pp. 13–47. Springer, London (2011)CrossRefGoogle Scholar
  26. 26.
    Pentland, A.P.: Fractal-based description of natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 661–674 (1984)CrossRefGoogle Scholar
  27. 27.
    Ivanovici, M., Richard, N.: Fractal dimension of color fractal images. IEEE Transactions on Image Processing 20(1), 227–235 (2011)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Backes, A.R., Casanova, D., Bruno, O.M.: Color texture analysis based on fractal descriptors. Pattern Recognition 45(5), 1984–1992 (2012)CrossRefGoogle Scholar
  29. 29.
    Brainard, D.H.: Color constancy. In: The Visual Neurosciences, pp. 948–961. MIT Press (2004)Google Scholar
  30. 30.
    Finlayson, G.D., Hordley, S.D.: Color constancy at a pixel. J. Opt. Soc. Am. A 18(2), 253–264 (2001)CrossRefGoogle Scholar
  31. 31.
    Jain, A., Healey, G.: A multiscale representation including opponent color features for texture recognition. IEEE Transactions on Image Processing 7(1), 124–128 (1998)CrossRefGoogle Scholar
  32. 32.
    Thai, B., Healey, G.: Modeling and classifying symmetries using a multiscale opponent color representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1224–1235 (1998)CrossRefGoogle Scholar
  33. 33.
    Funt, B.V., Finlayson, G.D.: Color constant color indexing. IEEE Trans. Pattern Anal. Mach. Intell. 17(5), 522–529 (1995)CrossRefGoogle Scholar
  34. 34.
    Adjeroh, D.A., Lee, M.C.: On ratio-based color indexing. Trans. Img. Proc. 10(1), 36–48 (2001)MATHCrossRefGoogle Scholar
  35. 35.
    Hordley, S.D., Finlayson, G.D., Schaefer, G., Tian, G.Y.: Illuminant and device invariant colour using histogram equalisation. Pattern Recognition 38 (2005)Google Scholar
  36. 36.
    Ojala, T., Pietikäinen, M., Mänepää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  37. 37.
  38. 38.
    Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex-new framework for empirical evaluation of texture analysis algorithms. In: 16th International Conference on Pattern Recognition, vol. 1, pp. 701–706 (2002)Google Scholar
  39. 39.
    Ohta, Y., Kanade, T., Sakai, T.: Color information for region segmentation. Computer Graphics and Image Processing 13(3), 222–241 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claudio Cusano
    • 1
  • Paolo Napoletano
    • 2
  • Raimondo Schettini
    • 2
  1. 1.Università degli Studi di PaviaPaviaItaly
  2. 2.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)Università degli Studi di Milano-BicoccaMilanoItaly

Personalised recommendations